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Example: Neural network CT segmentation
Erik Smistad edited this page Aug 6, 2019
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1 revision
This example uses a neural network to segment a CT thorax volume using a neural network. The result is renderered using GPU-based ray casting as shown above.
/**
* Examples/Segmentation/neuralNetworkCTSegmentation.cpp
*
* If you edit this example, please also update the wiki and source code file in the repository.
*/
#include <FAST/Tools/CommandLineParser.hpp>
#include <FAST/Importers/ImageFileImporter.hpp>
#include <FAST/Visualization/SimpleWindow.hpp>
#include <FAST/Algorithms/ImagePatch/PatchGenerator.hpp>
#include <FAST/Algorithms/ImagePatch/PatchStitcher.hpp>
#include <FAST/Algorithms/NeuralNetwork/SegmentationNetwork.hpp>
#include <FAST/Algorithms/TissueSegmentation/TissueSegmentation.hpp>
#include <FAST/Visualization/VolumeRenderer/AlphaBlendingVolumeRenderer.hpp>
#include <FAST/Visualization/VolumeRenderer/ThresholdVolumeRenderer.hpp>
#include <FAST/Algorithms/NeuralNetwork/InferenceEngineManager.hpp>
using namespace fast;
int main(int argc, char** argv) {
Reporter::setGlobalReportMethod(Reporter::COUT);
CommandLineParser parser("Neural network CT volume segmentation example");
parser.addChoice("inference-engine",
{"default", "TensorFlowCPU", "TensorFlowCUDA", "TensorFlowROCm"},
"default",
"Which neural network inference engine to use");
parser.addPositionVariable(1,
"filename",
Config::getTestDataPath() + "/CT/LIDC-IDRI-0072/000001.dcm",
"CT volume to process");
parser.parse(argc, argv);
auto importer = ImageFileImporter::New();
importer->setFilename(parser.get("filename"));
auto generator = PatchGenerator::New();
generator->setPatchSize(512, 512, 32);
generator->setInputConnection(importer->getOutputPort());
generator->enableRuntimeMeasurements();
auto network = SegmentationNetwork::New();
if(parser.get("inference-engine") != "default") {
network->setInferenceEngine(parser.get("inference-engine"));
} else {
if(InferenceEngineManager::isEngineAvailable("TensorFlowCUDA")) {
network->setInferenceEngine("TensorFlowCUDA");
} else {
network->setInferenceEngine("TensorFlowCPU");
}
}
const auto engine = network->getInferenceEngine()->getName();
network->load(Config::getTestDataPath() + "/NeuralNetworkModels/lung_nodule_segmentation.pb");
network->setMinAndMaxIntensity(-1200.0f, 400.0f);
network->setScaleFactor(1.0f / (400 + 1200));
network->setMeanAndStandardDeviation(-1200.0f, 1.0f);
network->setOutputNode(0, "conv3d_14/truediv");
network->setInputConnection(generator->getOutputPort());
network->setResizeBackToOriginalSize(true);
network->setThreshold(0.3);
network->enableRuntimeMeasurements();
auto stitcher = PatchStitcher::New();
stitcher->setInputConnection(network->getOutputPort());
stitcher->enableRuntimeMeasurements();
auto renderer = AlphaBlendingVolumeRenderer::New();
renderer->setTransferFunction(TransferFunction::CT_Blood_And_Bone());
renderer->addInputConnection(importer->getOutputPort());
auto renderer2 = ThresholdVolumeRenderer::New();
renderer2->addInputConnection(stitcher->getOutputPort());
auto window = SimpleWindow::New();
window->addRenderer(renderer);
window->addRenderer(renderer2);
window->start();
}
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